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Migrating a Privacy-Safe Information Extraction System to a Software 2.0 Design

Summary: Case study converting Gmail's privacy-safe, production rule-based IE to Software 2.0: use rule outputs as training labels to build ML extractors that improve precision/recall, shrink codebase, and enable cross-language extraction. Discusses challenges in training-data generation/management, model evaluation, and necessary Software‑1.0 infrastructure to safely deploy ML extractors. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
368
Venue
CIDR
Year
2020
Pagerank
4.1945683e-05
Overall Rank
11,543 | 19.70%
DOI
-

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